The Bazhenov Lab at the University of California, San Diego, is currently seeking to fill a postdoctoral position to study mechanisms of plasticity and learning in the insect olfactory system. This exciting project involves close collaboration with several experimental labs. The ultimate goals of the work are to advance our understanding of how olfactory information is encoded, how animals learn from limited experience, and to develop novel AI algorithms inspired by principles learned from nature.

The successful candidate will be responsible for: (a) Designing anatomically realistic computational network models of the olfactory system based on experimental data; (b) Developing and training machine learning models using empirical data. These models will be instrumental in uncovering network dynamics involved in processing and learning olfactory inputs.

Additionally, depending on the candidate's interests and experience, there may be opportunities to participate in other related lab projects, such as modeling sleep or applying principles learned from neuroscience to artificial intelligence for continuous learning, knowledge generalization, and adaptation to novel situations and contexts.

An ideal candidate should have a background in computational/theoretical neuroscience and neural modeling. Programming experience with C/C++ is required, and knowledge of Python and PyTorch is a significant plus.

The University of California offers excellent benefits, and the salary will be based on research experience. Applicants should send a brief statement of research interests, a CV, and the names of three references to Maxim Bazhenov at mbazhenov@ucsd.edu